Deep Belief Networks (DBN) for Financial Time Series Analysis and Market Trends Prediction

Deep Belief Networks (DBN) for Financial Time Series Analysis and Market Trends Prediction

Authors

  • Runze Song Information System & Technology Data Analytics, California State University, CA, USA
  • Zeyu Wang Computer Science, University of Toronto, Toronto, Canada
  • Lingfeng Guo Business Analytics, Trine University, AZ, USA
  • Fanyi Zhao Computer Science, Stevens Institute of Technology, NJ, USA
  • Zeqiu Xu Information Networking, Carnegie Mellon University, PA, USA

DOI:

https://doi.org/10.53469/wjimt.2024.07(04).01

Keywords:

Deep Belief Networks (DBNs), Financial Time Series Analysis, Market Trends Prediction, Deep Learning

Abstract

Deep Belief Networks (DBNs) represent a transformative approach in financial time series analysis, addressing the complexities of market dynamics through advanced deep learning techniques. By leveraging hierarchical layers of unsupervised Restricted Boltzmann Machines (RBMs), DBNs excel in extracting intricate patterns from vast datasets, enabling accurate prediction of market trends and fluctuations. This capability not only enhances traditional financial analysis methods but also facilitates informed decision-making in dynamic and uncertain financial environments.

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Published

2024-07-08
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